Artificial Intelligence in Clinical Handover: A Scoping Review of Applications, Technologies, and Outcomes Across Healthcare Settings

SHARUN NV, Rohini T, Athar Javeth

Background: Clinical handover (handoff) is the structured transfer of patient information, professional responsibility, and accountability between healthcare providers during transitions of care. Ineffective handovers contribute substantially to communication failures, medical errors, treatment delays, preventable adverse events, and compromised patient safety. Healthcare organizations increasingly recognize standardized communication frameworks such as SBAR, ISBAR, IPASS, and structured electronic handover tools. Despite these advances, handover processes remain vulnerable because clinicians must synthesize large volumes of complex clinical information under considerable time pressure. Recent advances in Artificial Intelligence (AI), particularly Large Language Models (LLMs), Generative Artificial Intelligence (GenAI), Natural Language Processing (NLP), machine learning, conversational AI, virtual reality (VR), and ambient AI scribes, have introduced novel approaches to improving clinical handovers. Emerging applications include automated handover documentation, AI-generated summaries, intelligent decision support, simulation-based education, chatbot-assisted learning, workflow automation, and communication quality assessment. Early studies suggest improvements in documentation efficiency, educational outcomes, workflow optimization, and communication quality, while also identifying concerns regarding hallucinations, explainability, clinician oversight, data privacy, and patient safety. Although individual studies have evaluated specific AI technologies in nursing, emergency medicine, perioperative care, and acute care settings, there has been no comprehensive mapping of the evidence across healthcare disciplines. A scoping review is therefore appropriate to identify the breadth of available evidence, summarize current applications, identify implementation approaches, and highlight research gaps. Review Objectives:- The review aims to, 1. Identify AI technologies used in clinical handover. 2. Describe clinical settings in which AI-assisted handover has been implemented or evaluated. 3. Map the applications of AI during clinical handover. 4. Summarize reported outcomes associated with AI-assisted handover. 5. Identify barriers, facilitators, ethical issues, and implementation considerations. 6. Identify knowledge gaps requiring future research.